We also compared MKIS-Net both qualitatively and quantitatively to light-weight networks recently presented in literature. To this end, we used the two retinal vessel segmentation datasets DRIVE and CHASE. The qualitative results (Figs. REF  and REF ) comparing MKIS-Net with ERFNet [1]} and M2U-Net [2]} are consistent with the findings in the previous subsection, in the sense that our network does deliver good segmentation results, preserving detail and coping with complex, thin vessel structures. We performed a quantitative comparison (Table REF ) of these nets in terms of the model size (in MB), number of parameters (Params), multiply and add operations in billions (MAdds), accuracy and F1 score, so as to gauge the computational requirements versus segmentation performance. Note that MKIS-Net yields more than 2% improvement in segmentation performance while being more than 3.5 times smaller parameter-wise as compared with the second smallest alternative (M2U-Net) in this comparison.
<FIGURE><FIGURE><TABLE><TABLE>